Title:
Character-level Convolutional Networks for Text Classification

Abstract: This article offers an empirical exploration on the use of character-level
convolutional networks (ConvNets) for text classification. We constructed
several large-scale datasets to show that character-level convolutional
networks could achieve state-of-the-art or competitive results. Comparisons are
offered against traditional models such as bag of words, n-grams and their
TFIDF variants, and deep learning models such as word-based ConvNets and
recurrent neural networks.

Comments:

An early version of this work entitled "Text Understanding from Scratch" was posted in Feb 2015 as arXiv:1502.01710. The present paper has considerably more experimental results and a rewritten introduction. Advances in Neural Information Processing Systems 28 (NIPS 2015)